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findSeedCutFlowTrackPy.py
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########################################################################################################################
##### code to make seeds from list of tracks #####
##### The track list must have this order: <BX> <pdgID> <trackId> <stave> <x> <y> <E> <weight> #####
##### Run this code: python findSeed.py -l <trackList.txt> -s <bool, true for signal positron> -e <energyCutinKeV> #####
##### written by: [email protected] #####
########################################################################################################################
import os
import sys
import time
import pprint
import math
from ROOT import *
from collections import OrderedDict
import argparse
import csv, array
import numpy as np
### needed for plotting of tracks and dimension from the spreadsheet
from makeTrackDiagrams import *
### the seed energy width
EseedMin = 2.0 # GeV
EseedMax = 13.0 # GeV
EseedMinPrelim = 0.2 # GeV
EseedMaxPrelim = 18.0 # GeV
### the seed pY width
PseedMin = -0.005 # GeV
PseedMax = 0.005 # GeV
### cut on nseeds
nseedsNoFitMax = 50
nseedsNoFitMax2 = 320
#//MeV mass of electron/positron
meMeV = 0.5109989461
meGeV = meMeV/1000.
### me^2 in GeV
meGeV2 = meGeV*meGeV
### the width of the road around the pivot tracks
yAbsMargins = 0.13 # mm (a "road" of 200 microns around the line between r4 and r1)
xAbsMargins = 0.13 # mm (a "road" of 200 microns around the line between r4 and r1)
zPositionListInner = [z2inner, z3inner]
zPositionListOuter = [z2outer, z3outer]
ddList = []
#### This is the cutflow dictionary
cutFlowDict = OrderedDict(
[('noCut', 0),
('x1Gtx4',0),
('x1*x4Negative',0),
('z1Eqz4', 0),
('yDipoleExitGt5p4',0),
('xDipoleExitLt25',0),
('xDipoleExitGt165', 0),
('xDipoleExitLt0',0),
('seedEnergy', 0),
('checkClusterTracksMiddleLayers', 0),
('checkClusterFit', 0),
('trackEnergy', 0),
('checkClusterXDistance', 0),
('checkClusterTrackPy', 0),
('checkClusterTrackPyLoose', 0),
('checkClusterTrackPyTight', 0)]
)
histos = {'hSVDValues2Global': TH1D("hSVDValues2Global", "output of SVD[1]; fit quality [1]; Events", 200, 0, 0.3),
'hSVDValues3Global': TH1D("hSVDValues3Global", "output of SVD[2]; fit quality [2]; Events", 1000, 0, 0.1),
'hSVDValues2LooseGlobal': TH1D("hSVDValues2LooseGlobal", "output of SVD[1]; fit quality [1]; Events", 200, 0, 0.3),
'hSVDValues3LooseGlobal': TH1D("hSVDValues3LooseGlobal", "output of SVD[2]; fit quality [2]; Events", 1000, 0, 0.1),
'hSVDValues2TightGlobal': TH1D("hSVDValues2TightGlobal", "output of SVD[1]; fit quality [1]; Events", 200, 0, 0.3),
'hSVDValues3TightGlobal': TH1D("hSVDValues3TightGlobal", "output of SVD[2]; fit quality [2]; Events", 1000, 0, 0.1),
'hSeedDistanceGlobal': TH1D("hSeedDistanceGlobal", "seed distance wrt analytical line; d [m]; Entries", 500, 0, 0.1),
'hSeedDistanceLooseGlobal': TH1D("hSeedDistanceLooseGlobal", "seed distance wrt analytical line (loose); d [m]; Entries", 500, 0, 0.10),
'hSeedDistanceTightGlobal': TH1D("hSeedDistanceTightGlobal", "seed distance wrt analytical line (tight); d [m]; Entries", 500, 0, 0.10),
'hSVDValues2TightVsEnergyGlobal': TH2D("hSVDValues2TightVsEnergyGlobal", "output of SVD[1]; Energy [GeV]; fit quality [1]; Events", 35, 0, 17.5, 100, 0, 0.3),
'hSeedDistanceTightVsEnergyGlobal': TH2D("hSeedDistanceTightVsEnergyGlobal", "seed distance wrt analytical line (tight) vs Energy; Energy [GeV]; d[m]; Entries", 35, 0, 17.5, 100, 0, 0.10),
### d parameter N-1 plot
'hNminus1d_High_Multiplicity_ELt4_TrackInclusive': TH1D('hNminus1d_High_Multiplicity_ELt4_TrackInclusive', "outlier d, high multiplicity, E < 4, all tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_High_Multiplicity_EGt4_TrackInclusive': TH1D('hNminus1d_High_Multiplicity_EGt4_TrackInclusive', "outlier d, high multiplicity, E > 4, all tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Medium_Multiplicity_ELt4_TightTrack': TH1D('hNminus1d_Medium_Multiplicity_ELt4_TightTrack', "outlier d, medium multiplicity, E < 4, tight tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Medium_Multiplicity_EGt4_TightTrack': TH1D('hNminus1d_Medium_Multiplicity_EGt4_TightTrack', "outlier d, medium multiplicity, E > 4, tight tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Medium_Multiplicity_ELt4_LooseTrack': TH1D('hNminus1d_Medium_Multiplicity_ELt4_LooseTrack', "outlier d, medium multiplicity, E < 4, loose tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Medium_Multiplicity_EGt4_LooseTrack': TH1D('hNminus1d_Medium_Multiplicity_EGt4_LooseTrack', "outlier d, medium multiplicity, E > 4, loose tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Low_Multiplicity_ELt4_TightTrack': TH1D('hNminus1d_Low_Multiplicity_ELt4_TightTrack', "outlier d, low multiplicity, E < 4, tight tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Low_Multiplicity_EGt4_TightTrack': TH1D('hNminus1d_Low_Multiplicity_EGt4_TightTrack', "outlier d, low multiplicity, E > 4, tight tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Low_Multiplicity_ELt4_LooseTrack': TH1D('hNminus1d_Low_Multiplicity_ELt4_LooseTrack', "outlier d, low multiplicity, E < 4, loose tracks; d[m]; Entries", 500, 0, 0.10),
'hNminus1d_Low_Multiplicity_EGt4_LooseTrack': TH1D('hNminus1d_Low_Multiplicity_EGt4_LooseTrack', "outlier d, low multiplicity, E > 4, loose tracks; d[m]; Entries", 500, 0, 0.10),
#### p_Y parameter N-1 plots
'hNminus1Py_High_Medium_Multiplicity_EInclusive_TrackInclusive': TH1D('hNminus1Py_High_Medium_Multiplicity_EInclusive_TrackInclusive', "track p_{Y}, high and medium multiplicity, All E, All Tracks; p'_Y [GeV]; Entries",100, -0.02, 0.02),
'hNminus1Py_Low_Multiplicity_ELt4_TightTracks': TH1D('hNminus1Py_Low_Multiplicity_ELt4_TightTracks', "track p_{Y}, low multiplicity, E < 4, tight tracks; p'_Y [GeV]; Entries",100, -0.02, 0.02),
'hNminus1Py_Low_Multiplicity_ELt4_LooseTracks': TH1D('hNminus1Py_Low_Multiplicity_ELt4_LooseTracks', "track p_{Y}, low multiplicity, E < 4, loose tracks; p'_Y [GeV]; Entries",100, -0.02, 0.02),
'hNminus1Py_Low_Multiplicity_EGt4_TightTracks': TH1D('hNminus1Py_Low_Multiplicity_EGt4_TightTracks', "track p_{Y}, low multiplicity, E > 4, tight tracks; p'_Y [GeV]; Entries",100, -0.06, 0.06),
'hNminus1Py_Low_Multiplicity_EGt4_LooseTracks': TH1D('hNminus1Py_Low_Multiplicity_EGt4_LooseTracks', "track p_{Y}, low multiplicity, E > 4, loose tracks; p'_Y [GeV]; Entries",100, -0.07, 0.07),
### fit parameter N-1 plots
'hNminus1FitPar1_High_Multiplicity_ELt4_TrackInclusive': TH1D('hNminus1FitPar1_High_Multiplicity_ELt4_TrackInclusive','output of SVD[1], high multiplicity, E < 4, inclusive tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_High_Multiplicity_ELt4_TrackInclusive': TH1D('hNminus1FitPar2_High_Multiplicity_ELt4_TrackInclusive','output of SVD[2], high multiplicity, E < 4, inclusive tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_High_Multiplicity_EGt4_TrackInclusive': TH1D('hNminus1FitPar1_High_Multiplicity_EGt4_TrackInclusive','output of SVD[1], high multiplicity, E > 4, inclusive tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_High_Multiplicity_EGt4_TrackInclusive': TH1D('hNminus1FitPar2_High_Multiplicity_EGt4_TrackInclusive','output of SVD[2], high multiplicity, E > 4, inclusive tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_Medium_Multiplicity_ELt4_TrackInclusive': TH1D('hNminus1FitPar1_Medium_Multiplicity_ELt4_TrackInclusive','output of SVD[1], Medium multiplicity, E < 4, inclusive tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_Medium_Multiplicity_ELt4_TrackInclusive': TH1D('hNminus1FitPar2_Medium_Multiplicity_ELt4_TrackInclusive','output of SVD[2], Medium multiplicity, E < 4, inclusive tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_Medium_Multiplicity_EGt4_TrackInclusive': TH1D('hNminus1FitPar1_Medium_Multiplicity_EGt4_TrackInclusive','output of SVD[1], Medium multiplicity, E > 4, inclusive tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_Medium_Multiplicity_EGt4_TrackInclusive': TH1D('hNminus1FitPar2_Medium_Multiplicity_EGt4_TrackInclusive','output of SVD[2], Medium multiplicity, E > 4, inclusive tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_Low_Multiplicity_ELt4_TightTrack': TH1D('hNminus1FitPar1_Low_Multiplicity_ELt4_TightTrack','output of SVD[1], low multiplicity, E < 4, tight tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_Low_Multiplicity_ELt4_TightTrack': TH1D('hNminus1FitPar2_Low_Multiplicity_ELt4_TightTrack','output of SVD[2], low multiplicity, E < 4, tight tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_Low_Multiplicity_EGt4_TightTrack': TH1D('hNminus1FitPar1_Low_Multiplicity_EGt4_TightTrack','output of SVD[1], low multiplicity, E > 4, tight tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_Low_Multiplicity_EGt4_TightTrack': TH1D('hNminus1FitPar2_Low_Multiplicity_EGt4_TightTrack','output of SVD[2], low multiplicity, E > 4, tight tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_Low_Multiplicity_ELt4_LooseTrack': TH1D('hNminus1FitPar1_Low_Multiplicity_ELt4_LooseTrack','output of SVD[1], low multiplicity, E < 4, Loose tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_Low_Multiplicity_ELt4_LooseTrack': TH1D('hNminus1FitPar2_Low_Multiplicity_ELt4_LooseTrack','output of SVD[2], low multiplicity, E < 4, Loose tracks; fit quality [2]; Events', 2000, -0.2, 0.2),
'hNminus1FitPar1_Low_Multiplicity_EGt4_LooseTrack': TH1D('hNminus1FitPar1_Low_Multiplicity_EGt4_LooseTrack','output of SVD[1], low multiplicity, E > 4, Loose tracks; fit quality [1]; Events', 200, 0, 0.3),
'hNminus1FitPar2_Low_Multiplicity_EGt4_LooseTrack': TH1D('hNminus1FitPar2_Low_Multiplicity_EGt4_LooseTrack','output of SVD[2], low multiplicity, E > 4, Loose tracks; fit quality [2]; Events', 2000, -0.2, 0.2)
}
### read the dEdX vs E curve for the electron
results = csv.reader(open("dEdXSilicon_ForElectron.csv"), delimiter=",")
xdEdX = array.array('f',[]); ydEdX = array.array('f', [])
for eachRow in results:
xdEdX.append(float(eachRow[0]))
ydEdX.append(float(eachRow[1]))
### This is to find the dEdx
def getdEdX(energy):
### the dEdX vs E curve, x axis energy is in MeV, dEdX in y is in keV/um, remember the unit difference
mevEnergy = energy*1e3
interpolateddEdX = np.interp(mevEnergy, xdEdX, ydEdX)
### interpolateddEdX in keV/um
return interpolateddEdX
### This is to find the absorbed energy in the FPC layer and cooling region
def energyAbsorbed(staveId, energy, vtxZ):
if(staveId == 0 or staveId == 8):
zPos = z1inner
elif(staveId == 1 or staveId == 9):
zPos = z1outer
elif(staveId == 2 or staveId == 10):
zPos = z2inner
elif(staveId == 3 or staveId == 11):
zPos = z2outer
elif(staveId == 4 or staveId == 12):
zPos = z3inner
elif(staveId == 5 or staveId == 13):
zPos = z3outer
elif(staveId == 6 or staveId == 14):
zPos = z4inner
elif(staveId == 7 or staveId == 15):
zPos = z4outer
else:
print("No suitable stave found!!! Exiting")
quit()
### front side
if((zPos - vtxZ) > 0):
absorbingFactor = 150
### back side
else:
absorbingFactor = 250
### this dEdX is in keV/um, multiply by the width to get the energy loss
dEdX = getdEdX(energy)
absorbed = dEdX*absorbingFactor
remainingEnergy = energy*1e6 - absorbed
### print("staveId: ", staveId, " zPos: ", zPos, " vtxZ: ", vtxZ, " energy (keV): ", energy*1e6, "dEdX (keV/um): ", dEdX, " factor: ", absorbingFactor, " absorbed (keV): ", absorbed, " remainingEnergy (keV): ", remainingEnergy)
return remainingEnergy
### functions needed for distance cut from the analytical line in x1 vs x4 plane
## get R in m given momentum p
def getR(p):
R = p/(0.3*B)
return R
### get R^2 in m^2 given momentum p
def getR2(p):
R = getR(p)
return R*R
### get the xTangent in m given ZTangent in m and momentum p
def getXTangent(ZT,p):
R2 = getR2(p)
XT = (R2/LB-ZT)*(LB/math.sqrt(R2-LB2))
return XT
### get x in Sasha's coordinate in m given zTangent and momentum p
def getx(ZT,p):
XT = getXTangent(ZT,p)
R = getR(p)
x = R-XT
return x
### get r1 and r2 (x,y --> 2 points) given the Z0 and Z4. This is in m
def getR1R2(p1, p2):
r1 = [getx(Z0,p1), getx(Z4,p1)]
r2 = [getx(Z0,p2), getx(Z4, p2)]
#print("r1: ",r1, " r2: ",r2)
#quit()
return r1, r2
#### find the distance from the analytical line to the r1 and r2 point in m
def Distance(x0Test,x4Test,r1,r2):
aOb = -(r1[1]-r2[1])/(r1[0]-r2[0])
cOb = (r1[1]-r2[1])/(r1[0]-r2[0])*r1[0] - r1[1]
### now find the distance
# d = |a*x0Test + b*x4Test + c|/sqrt(a^2 + b^2) --> d = |(a/b)*x0Test + x4Test + (c/b)|/sqrt((a/b)^2 + 1^2)
d = abs(aOb*x0Test + x4Test + (cOb))/math.sqrt(aOb*aOb + 1)
return d
# get the unit vector along one vector
def rUnit2(r1, r2):
r = (r2-r1).Unit()
return r
### get the 3d line
def line3d(z,m_xz,c_xz,m_yz,c_yz):
# the intersection of those two planes and
# the function for the line would be:
# or:
x = (z - c_xz)/m_xz
y = (z - c_yz)/m_yz
return x,y
### get the TLorentzVector of the seed
def getSeedMomentum(r1, r4):
#### find the energy of the tracks
x0 = 0
z0 = zofx(r1, r4, x0)
xExit = abs(xofz(r1, r4, zDipoleActiveExit))
yExit = yofz(r1, r4, zDipoleActiveExit)
H = abs((zDipoleActiveExit-z0))*mm2m ### converting H from mm to m
xExitInM = xExit*mm2m ### converting xExit from mm to m
R = H*(LB)/xExitInM + xExitInM ### // This is the radius of curvature for a track
P = 0.3*B*R ### here B in Tesla and R in m
v1 = TVector2(r1[2], r1[1])
v4 = TVector2(r4[2], r4[1])
u = rUnit2(v1, v4)
uz = u.X()
uy = u.Y()
px = 0
py = P*uy
pz = P*uz
### The seed lorentz vector, need for energy plotting
p = TLorentzVector()
p.SetPxPyPzE(px, py, pz, math.sqrt(px*px + py*py + pz*pz + meGeV2))
return p, xExit, yExit
#### fit with chi2
def seed3dChi2fit(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4):
xList = [r1[0],r4[0]]
yList = [r1[1],r4[1]]
zList = [r1[2],r4[2]]
if len(r2Inner)>0:
xList.append(r2Inner[0])
yList.append(r2Inner[1])
zList.append(r2Inner[2])
if len(r2Outer)>0:
xList.append(r2Outer[0])
yList.append(r2Outer[1])
zList.append(r2Outer[2])
if len(r3Inner)>0:
xList.append(r3Inner[0])
yList.append(r3Inner[1])
zList.append(r3Inner[2])
if len(r3Outer)>0:
xList.append(r3Outer[0])
yList.append(r3Outer[1])
zList.append(r3Outer[2])
x = np.array(xList)
y = np.array(yList)
z = np.array(zList)
# this will find the slope and x-intercept of a plane
# parallel to the y-axis that best fits the data
A_xz = np.vstack((x, np.ones(len(x)))).T
result_xz = np.linalg.lstsq(A_xz, z,rcond=None)
m_xz, c_xz = result_xz[0]
residuals_xz = result_xz[1]
# again for a plane parallel to the x-axis
A_yz = np.vstack((y, np.ones(len(y)))).T
result_yz = np.linalg.lstsq(A_yz, z,rcond=None)
m_yz, c_yz = result_yz[0]
residuals_yz = result_yz[1]
zz = np.array([zDipoleActiveExit, r4[2]])
xx,yy = line3d(zz, m_xz, c_xz, m_yz, c_yz)
lfit = TPolyLine3D()
for i in range(2):
lfit.SetNextPoint(zz[i],yy[i],xx[i])
return residuals_xz,residuals_yz, lfit
### use svd fit
def seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4):
xList = [r1[0],r4[0]]
yList = [r1[1],r4[1]]
zList = [r1[2],r4[2]]
if len(r2Inner)>0:
xList.append(r2Inner[0])
yList.append(r2Inner[1])
zList.append(r2Inner[2])
if len(r2Outer)>0:
xList.append(r2Outer[0])
yList.append(r2Outer[1])
zList.append(r2Outer[2])
if len(r3Inner)>0:
xList.append(r3Inner[0])
yList.append(r3Inner[1])
zList.append(r3Inner[2])
if len(r3Outer)>0:
xList.append(r3Outer[0])
yList.append(r3Outer[1])
zList.append(r3Outer[2])
x = np.array(xList)
y = np.array(yList)
z = np.array(zList)
data = np.concatenate((x[:, np.newaxis],
y[:, np.newaxis],
z[:, np.newaxis]),
axis=1)
# Calculate the mean of the points, i.e. the 'center' of the cloud
datamean = data.mean(axis=0)
# Do an SVD on the mean-centered data (Singular Value Decomposition)
uu, dd, vv = np.linalg.svd(data - datamean)
# Now vv[0] contains the first principal component, i.e. the direction
# vector of the 'best fit' line in the least squares sense.
# Now generate some points along this best fit line, for plotting.
# I use -7, 7 since the spread of the data is roughly 14
# and we want it to have mean 0 (like the points we did
# the svd on). Also, it's a straight line, so we only need 2 points.
# linepts = vv[0] * np.mgrid[-7:7:2j][:, np.newaxis]
linepts = vv[0] * np.mgrid[-170:170:2j][:, np.newaxis]
# shift by the mean to get the line in the right place
linepts += datamean
#print("linepoints: ", linepts)
return linepts, dd ## dd is a 1D array of the data singular values
### draw the SVD fit
def drawFit(name,linepts,hits):
g = TGraph2D()
g.SetMarkerSize(3)
g.SetMarkerStyle(20)
g.SetMarkerColor(ROOT.kRed)
g.SetLineColor(ROOT.kRed)
for i in range(len(hits)):
g.SetPoint(i,hits[i][0],hits[i][1],hits[i][2])
g.GetXaxis().SetRangeUser(0, 650)
g.GetYaxis().SetRangeUser(-10,+10)
g.GetZaxis().SetRangeUser(3900, 4400)
lfit = TPolyLine3D()
for point in linepts:
lfit.SetNextPoint(point[0],point[1],point[2])
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",2000,2000)
view = TView.CreateView(1)
xviewmin = 3900 #if(isel(r1[0])) else xPsideL
xviewmax = 4200 #xEsideR if(isel(r1[0])) else 0
#view.SetAutoRange()
view.SetRange(0,-0.8, xviewmin , 650,+0.8,xviewmax)
view.ShowAxis()
g.Draw("p0")
lfit.Draw("same")
cnv.SaveAs(name)
### get the expected number of hits in the layer 2 and layer 3 of the tracker given the position of the track
def getExpectedHits(r1, r4, energy):
global xAbsMargins, yAbsMargins
if(energy < 4.0):
xAbsMargins = 0.22
yAbsMargins = 0.22
else:
xAbsMargins = 0.13
yAbsMargins = 0.13
expectedHits2Inner = 0
expectedHits2Outer = 0
expectedHits3Inner = 0
expectedHits3Outer = 0
r1min = [r1[0]-xAbsMargins, r1[1]-yAbsMargins, r1[2]]
r1max = [r1[0]+xAbsMargins, r1[1]+yAbsMargins, r1[2]]
r4min = [r4[0]-xAbsMargins, r4[1]-yAbsMargins, r4[2]]
r4max = [r4[0]+xAbsMargins, r4[1]+yAbsMargins, r4[2]]
# check possible clusters in layer 2, for both inner and outer stave
x2minInner = xofz(r1min, r4min, z2inner)
x2maxInner = xofz(r1max, r4max, z2inner)
x2minOuter = xofz(r1min, r4min, z2outer)
x2maxOuter = xofz(r1max, r4max, z2outer)
#/// check possible clusters in layer 3, for both inner and outer stave
x3minInner = xofz(r1min, r4min, z3inner)
x3maxInner = xofz(r1max, r4max, z3inner)
x3minOuter = xofz(r1min, r4min, z3outer)
x3maxOuter = xofz(r1max, r4max, z3outer)
x2Inner = xofz(r1, r4, z2inner)
x2Outer = xofz(r1, r4, z2outer)
x3Inner = xofz(r1, r4, z3inner)
x3Outer = xofz(r1, r4, z3outer)
for chipName, boundaries in xBoundaries.items():
if "layerid2" in chipName and x2Inner > boundaries[0] and x2Inner < boundaries[1]:
expectedHits2Inner += 1
if "layerid3" in chipName and x2Outer > boundaries[0] and x2Outer < boundaries[1]:
expectedHits2Outer += 1
if "layerid4" in chipName and x3Inner > boundaries[0] and x3Inner < boundaries[1]:
expectedHits3Inner += 1
if "layerid5" in chipName and x3Outer > boundaries[0] and x3Outer < boundaries[1]:
expectedHits3Outer += 1
return expectedHits2Inner, expectedHits2Outer, expectedHits3Inner, expectedHits3Outer
### check if there are hits along one track in layer 2 and layer 3
def check_clusters(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side, energy):
global ddList, xAbsMargins, yAbsMargins
expectedHits2Inner, expectedHits2Outer, expectedHits3Inner, expectedHits3Outer = getExpectedHits(r1, r4, energy)
hitsOnRoad2Inner = 0 ### how many tracks accepted in the road along the r1 and r4
hitsOnRoad2Outer = 0
hitsOnRoad3Inner = 0 ### how many tracks accepted in the road along the r1 and r4
hitsOnRoad3Outer = 0
if(energy < 4.0):
xAbsMargins = 0.22
yAbsMargins = 0.22
else:
xAbsMargins = 0.13
yAbsMargins = 0.13
r1min = [r1[0]-xAbsMargins, r1[1]-yAbsMargins, r1[2]]
r1max = [r1[0]+xAbsMargins, r1[1]+yAbsMargins, r1[2]]
r4min = [r4[0]-xAbsMargins, r4[1]-yAbsMargins, r4[2]]
r4max = [r4[0]+xAbsMargins, r4[1]+yAbsMargins, r4[2]]
# check possible clusters in layer 2, for both inner and outer stave
y2minInner = yofz(r1min, r4min, z2inner)
y2maxInner = yofz(r1max, r4max, z2inner)
x2minInner = xofz(r1min, r4min, z2inner)
x2maxInner = xofz(r1max, r4max, z2inner)
y2minOuter = yofz(r1min, r4min, z2outer)
y2maxOuter = yofz(r1max, r4max, z2outer)
x2minOuter = xofz(r1min, r4min, z2outer)
x2maxOuter = xofz(r1max, r4max, z2outer)
### separate out staves for allR2, work on the inner stave of layer 2
accept2Inner = False
accept2Outer = False
innerR2FromMatching = []
outerR2FromMatching = []
for i2 in range(0, len(allR2Inner)):
### remember allR2 has x, y, z and E saved in the list
accept2yzInner = ( (allR2Inner[i2][1] >= y2minInner and allR2Inner[i2][1] <= y2maxInner) )
if(not accept2yzInner): continue
accept2xzInner = ( ( allR2Inner[i2][0] >= x2minInner and allR2Inner[i2][0] <= x2maxInner ) )
if(not accept2xzInner): continue
accept2Inner = True
hitsOnRoad2Inner += 1
innerR2FromMatching.append(allR2Inner[i2]) ### collect all matched tracks from layer 2, remove the break
### separate out staves for allR2
for i2 in range(0, len(allR2Outer)):
### remember allR2 has x, y, z and E saved in the list
accept2yzOuter = ( (allR2Outer[i2][1] >= y2minOuter and allR2Outer[i2][1] <= y2maxOuter) )
if(not accept2yzOuter): continue
accept2xzOuter = ( ( allR2Outer[i2][0] >= x2minOuter and allR2Outer[i2][0] <= x2maxOuter ) )
if(not accept2xzOuter): continue
accept2Outer = True
hitsOnRoad2Outer += 1
outerR2FromMatching.append(allR2Outer[i2]) ### collect all matched tracks from layer 2, remove the break
#/// check possible clusters in layer 3, for both inner and outer stave
y3minInner = yofz(r1min, r4min, z3inner) ### z for inner stave layer 3 is 4064.5125
y3maxInner = yofz(r1max, r4max, z3inner)
x3minInner = xofz(r1min, r4min, z3inner)
x3maxInner = xofz(r1max, r4max, z3inner)
y3minOuter = yofz(r1min, r4min, z3outer) ### z for Outer stave layer 3 is 4076.5125
y3maxOuter = yofz(r1max, r4max, z3outer)
x3minOuter = xofz(r1min, r4min, z3outer)
x3maxOuter = xofz(r1max, r4max, z3outer)
### separate out staves for allR3, work on the inner stave of layer 3
accept3Inner = False
accept3Outer = False
innerR3FromMatching = []
outerR3FromMatching = []
for i3 in range(0, len(allR3Inner)):
### remember allR3 has x, y, z and E saved in the list
accept3yzInner = ( (allR3Inner[i3][1] >= y3minInner and allR3Inner[i3][1] <= y3maxInner) )
if(not accept3yzInner): continue
accept3xzInner = ( ( allR3Inner[i3][0] >= x3minInner and allR3Inner[i3][0] <= x3maxInner ) )
if(not accept3xzInner): continue
accept3Inner = True
hitsOnRoad3Inner += 1
innerR3FromMatching.append(allR3Inner[i3]) ### collect all matched tracks from layer 3, remove the break
#break
### separate out staves for allR3
for i3 in range(0, len(allR3Outer)):
### remember allR3 has x, y, z and E saved in the list
accept3yzOuter = ( (allR3Outer[i3][1] >= y3minOuter and allR3Outer[i3][1] <= y3maxOuter) )
if(not accept3yzOuter): continue
accept3xzOuter = ( ( allR3Outer[i3][0] >= x3minOuter and allR3Outer[i3][0] <= x3maxOuter ) )
if(not accept3xzOuter): continue
accept3Outer = True
hitsOnRoad3Outer += 1
outerR3FromMatching.append(allR3Outer[i3]) ### collect all matched tracks from layer 3, remove the break
### find the number of actual matched tracks
nMatched = (expectedHits2Inner<=hitsOnRoad2Inner)+(expectedHits2Outer<=hitsOnRoad2Outer)+(expectedHits3Inner<=hitsOnRoad3Inner)+(expectedHits3Outer<=hitsOnRoad3Outer)
### find the number of expected tracks
nExpected = expectedHits2Inner + expectedHits2Outer + expectedHits3Inner + expectedHits3Outer
### only if we have hit in one of the two inner layer of the tracker, we select the seed track, otherwise we reject the track
if(nMatched>=3):
return nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching
else:
return nMatched, nExpected, [], [], [], []
#### prepare the SVD fit for the seed track
def makeSeedFit(r1, r4, nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching, nseedsNoFit):
### now check if the SVD match works
allDDList = {'dd':[], 'linepts':[], 'i2Inner':[], 'i2Outer':[], 'i3Inner':[], 'i3Outer':[]}
#### number of matches tracks in each of the staves of the layer 2 and layer 3
n2i = len(innerR2FromMatching)
n2o = len(outerR2FromMatching)
n3i = len(innerR3FromMatching)
n3o = len(outerR3FromMatching)
if(n2i> 0) :
for i2Inner in range(n2i):
r2Inner = innerR2FromMatching[i2Inner]
if(n2o > 0):
for i2Outer in range(n2o):
r2Outer = outerR2FromMatching[i2Outer]
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r2Outer = []
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r2Inner = []
if(n2o > 0):
for i2Outer in range(n2o):
r2Outer = outerR2FromMatching[i2Outer]
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r2Outer = []
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
return False, {}
### start with a very high value, in the end we will take the smallest one
ddValue0 = 1e11
ddValue1 = 1e11
ddValue2 = 1e11
### the index of the best matched track
iWinner = -1
#print("ddList:", allDDList)
for i in range(len(allDDList['dd'])):
### calculate the seed energy
xDipoleFromFit = xofz(allDDList['linepts'][i][0], allDDList['linepts'][i][1], zDipoleExit)*mm2m
xLayer4FromFit = xofz(allDDList['linepts'][i][0], allDDList['linepts'][i][1], r4[2])*mm2m
yDipoleFromFit = yofz(allDDList['linepts'][i][0], allDDList['linepts'][i][1], zDipoleExit)*mm2m
yLayer4FromFit = yofz(allDDList['linepts'][i][0], allDDList['linepts'][i][1], r4[2])*mm2m
### here r1 and r4 must be in mm
R1 = [xDipoleFromFit*m2mm, yDipoleFromFit*m2mm, zDipoleExit]
R4 = [xLayer4FromFit*m2mm, yLayer4FromFit*m2mm, r4[2]]
#### re-evaluate the seed energy from the best fit track
pSeed, xExit, yExit = getSeedMomentum(R1, R4)
ddValue = allDDList['dd'][i]
#print("ddValue1", ddValue)
histos['hSVDValues2Global'].Fill(ddValue[1])
histos['hSVDValues3Global'].Fill(ddValue[2])
if(nMatched==4):
histos['hSVDValues2TightGlobal'].Fill(ddValue[1])
histos['hSVDValues3TightGlobal'].Fill(ddValue[2])
histos['hSVDValues2TightVsEnergyGlobal'].Fill(pSeed.E(),ddValue[1])
else:
histos['hSVDValues2LooseGlobal'].Fill(ddValue[1])
histos['hSVDValues3LooseGlobal'].Fill(ddValue[2])
#if ((0.0 < ddValue[1] < ddValueCut1) and (0.0 < ddValue[2] < 0.01)):
#if ((0.0 < ddValue[1] < 0.1) and (0.0 < ddValue[2] < 0.05)):
### printing out py
#print("-->before cut: ddValue1: ", ddValue[1], " ddValue2 :", ddValue[2], " and pSeed.E() ", pSeed.E(), " nseedsNoFit: ", nseedsNoFit, " nMatched: ",nMatched)
### high multiplicity
if(nseedsNoFit > nseedsNoFitMax2):
### energy < 4 GeV
if pSeed.E() < 4.0:
histos['hNminus1FitPar1_High_Multiplicity_ELt4_TrackInclusive'].Fill(ddValue[1])
histos['hNminus1FitPar2_High_Multiplicity_ELt4_TrackInclusive'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.08) and (0.0 < ddValue[2] < 0.02)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### energy > 4 GeV
else:
histos['hNminus1FitPar1_High_Multiplicity_EGt4_TrackInclusive'].Fill(ddValue[1])
histos['hNminus1FitPar2_High_Multiplicity_EGt4_TrackInclusive'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.05) and (0.0 < ddValue[2] < 0.01)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
#### medium multiplicity
elif(nseedsNoFitMax < nseedsNoFit <= nseedsNoFitMax2):
### energy < 4 GeV
if(pSeed.E()) < 4:
histos['hNminus1FitPar1_Medium_Multiplicity_ELt4_TrackInclusive'].Fill(ddValue[1])
histos['hNminus1FitPar2_Medium_Multiplicity_ELt4_TrackInclusive'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.06) and (0.0 < ddValue[2] < 0.05)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### energy > 4 GeV
else:
histos['hNminus1FitPar1_Medium_Multiplicity_EGt4_TrackInclusive'].Fill(ddValue[1])
histos['hNminus1FitPar2_Medium_Multiplicity_EGt4_TrackInclusive'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.06) and (0.0 < ddValue[2] < 0.06)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### low multiplicity
else:
### tight tracks
if(nMatched == 4):
### energy < 4 GeV
if(pSeed.E() < 4):
histos['hNminus1FitPar1_Low_Multiplicity_ELt4_TightTrack'].Fill(ddValue[1])
histos['hNminus1FitPar2_Low_Multiplicity_ELt4_TightTrack'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.1) and (0.0 < ddValue[2] < 0.1)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### energy > 4 GeV
else:
histos['hNminus1FitPar1_Low_Multiplicity_EGt4_TightTrack'].Fill(ddValue[1])
histos['hNminus1FitPar2_Low_Multiplicity_EGt4_TightTrack'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.1) and (0.0 < ddValue[2] < 0.1)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### loose tracks
else:
### energy < 4 GeV
if(pSeed.E() < 4.):
histos['hNminus1FitPar1_Low_Multiplicity_ELt4_LooseTrack'].Fill(ddValue[1])
histos['hNminus1FitPar2_Low_Multiplicity_ELt4_LooseTrack'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.2) and (0.0 < ddValue[2] < 0.05)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### energy > 4 GeV
else:
histos['hNminus1FitPar1_Low_Multiplicity_EGt4_LooseTrack'].Fill(ddValue[1])
histos['hNminus1FitPar2_Low_Multiplicity_EGt4_LooseTrack'].Fill(ddValue[2])
if ((0.0 < ddValue[1] < 0.065) and (0.0 < ddValue[2] < 0.065)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
#print("-->after cut: ddValue1: ", ddValue[1], " ddValue2 :", ddValue[2], " and pSeed.E() ", pSeed.E(), " nseedsNoFit: ", nseedsNoFit, " nMatched: ",nMatched)
### only if a good fit is available, return True
if iWinner >= 0:
#print("ddValue0: ", ddValue0, "ddValue1: ",ddValue1, "ddValue2: ", ddValue2)
return True, {"ddValue0": ddValue0, "ddValue1":ddValue1, "ddValue2": ddValue2, "linepts":allDDList['linepts'][iWinner], "r2Inner":allDDList['i2Inner'][iWinner], "r2Outer":allDDList['i2Outer'][iWinner], "r3Inner":allDDList['i3Inner'][iWinner], "r3Outer":allDDList['i3Outer'][iWinner]}
else:
return False, {}
### making the seeds from two tracks from innermost layer and outermost layer, check the seeding cuts in the process
def makeseedNoFit(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side):
if(abs(r1[0]) >= abs(r4[0])):
return False, {} ### |x1| must be smaller than |x4|
if(r1[0]*r4[0] < 0):
return False, {} ### the x value should have the same sign, i.e. on the same side of the beam
if(r1[2] == r4[2]):
return False, {} ### if z1=z4..., this is also impossible
yDipoleExit = yofz(r1, r4, zDipoleExit)
xDipoleExit = xofz(r1, r4, zDipoleExit)
### The following cuts are coming because of the distribution of the signal
### the following cannot be 10.8mm/2 according to the signal tracks
if(abs(yDipoleExit) > 10.8/2):
return False, {}
### This is according to the signal x:y at the dipole exit
if(abs(xDipoleExit) < 20.0):
return False, {} # the track should point to |x|<~1.0 at the dipole exit
if(abs(xDipoleExit) > 330.0/2):
return False, {}
### select only positive x or negative x accroding to the particle
if( (side=="Positron" and xDipoleExit < 0) or (side=="Electron" and xDipoleExit > 0)):
return False, {}
### get the momentum of the seed from the r1 and r4
p, xExit, yExit = getSeedMomentum(r1, r4)
### checking the track energy
if(p.E() < EseedMinPrelim or p.E() > EseedMaxPrelim):
return False, {}
### add a 2 GeV cut
if(p.E() < 2.0):
return False, {}
nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching = check_clusters(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side, p.E())
if nMatched < 3:
return False, {}
return True, {}
### making the seeds from two tracks from innermost layer and outermost layer, check the seeding cuts in the process
def makeseed(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side, r1GeV, r10GeV, useFit=1, nseedsNoFit=0):
cutFlowDict['noCut'] += 1
if(abs(r1[0]) >= abs(r4[0])):
return False, {} ### |x1| must be smaller than |x4|